New MIT Consortium Links Innovation With Real-World Biomanufacturing

A conversation with J. Christopher Love, MIT Initiative for New Manufacturing

A new academic/industry collaboration out of MIT is confronting the perceived gap between academic innovation and the rigid, practical realities of commercial production. The MIT Initiative for New Manufacturing (INM) is connecting a cross section of market-leading companies with each other and with MIT's academic departments campus-wide.

We spoke with J. Christopher Love, an MIT professor, entrepreneur, and an INM cofounder, about how the biopharma sector can learn from industries like semiconductors and electric vehicles. The group's founding members include Amgen and Sanofi, as well as Autodesk, Flex, GE Vernova, PTC, and Siemens.

He said while biopharma is ahead of other bioindustrial peers with widespread adoption of continuous processing and single-use technologies, it remains a cautious laggard when it comes to automation, digital tools, and agentic AI under the shadow of the regulatory, clinical, and financial risks. There's mounting hype, but these technologies are difficult to deploy mid-pipeline and not widely adopted, especially in the U.S.

This month he spoke at Kivi Bio's workforce conference in Kenosha, Wisconsin, about the INM and its workforce development arm, TechAMP. Ahead of his talk, he offered to answer our questions about the INM and its findings so far. The conversation has been edited for clarity.

The INM gets to the heart of a long-held belief in commercial pharma manufacturing that the academic class is out of touch with the practical realities of clinical and commercial biomanufacturing. Is there a middle ground where these two groups can come together?

Love: The MIT Initiative for New Manufacturing is a new effort across the campus to try to understand how we help the nation and, ultimately, the globe transform manufacturing to make it more productive with better jobs, higher wages, and more sustainability. We looked at the last 15 to 20 years and see that productivity across the country has flatlined in terms of output per labor. We also see that a lot of the innovations happening across the sector, both in academia and in small businesses in manufacturing technologies, aren't necessarily being adopted at the pace at which they could be transformative.

This initiative really started as a grassroots effort within the MIT community to try to understand where the advances are happening across sectors and how to take some of the best learnings from other sectors and apply them into particular spaces.

In biopharmaceutical and pharmaceutical manufacturing, there are things we do that are already really remarkable. The transformation toward continuous processing is definitely more advanced in the biopharmaceutical sector than in the bioindustrial sector, considering the former’s application of single-use technologies.

Yet compared to other industries, we still don't adopt automation and some of the digital tools that are more prevalent in advanced manufacturing or other sectors like semiconductors, which are pioneering aspects of that. So how do we start to learn?

MIT has had a long track record of advances in manufacturing, including in the biopharmaceutical sector. We've had a close relationship with the industry as it grew up producing recombinant proteins. Since then, in all the new modalities, our experience as academics has shown that it's best to ground those expectations or work in the science — in things that are going to be translationally relevant down the road.

We've had a history of interacting with the industry through consortia and safe-harbor meetings here at MIT to talk about where we see the future of manufacturing. INM is an extension of some of that infrastructure, but with a new emphasis on bringing the best technologies forward. This will require research in technologies and systems, but also an understanding of the digital-human interface that improves productivity and the work experience.

This is a place where there may be some disconnects. There are great technologies and innovations occurring within the academic system and in startup companies that don't necessarily reach their full potential in the manufacturing sector.

How do we bridge that gap? What are the right financing mechanisms? How do we think about the integration of these technologies? And then finally, how do we think about transforming the manufacturing base overall?

Biopharmaceuticals are still concentrated in a small number of manufacturers. This isn't true for other industries where we have small and medium enterprises that contribute to production. How can we bring that mindset to biomanufacturing?

Your background is in biomanufacturing. What are some of the questions or problems in our ecosystem that you want to solve?

Love: We've been discussing the role of agentic AI and newer approaches in regulated manufacturing environments. Where do they fit in biopharmaceuticals? Also, what are the research questions we need to think about together to help understand regulatory concerns, technical concerns, and strategies for implementation? We're looking across the entire manufacturing landscape.

Rolling out new technologies for approved drugs is daunting. No matter how many ways regulators encourage companies to try, companies resist.

Love: One hundred percent. We see this in other sectors. For example, GE Vernova works in airlines; this is a regulated industry. Many software companies like Autodesk or PTC touch on regulatory aspects. This is a theme we've identified: How do we think about these types of innovative leaps that are possible in manufacturing when we have regulatory requirements?

If I have an existing product, would I incorporate new technology to change that process? A lot of companies are hesitant to do so. Beyond that, if I'm a venture-backed company and I have a new asset, I don't necessarily want to multiply risk to my clinical ambitions with my technical risk on the manufacturing front, so I might wait.

One of the research areas that we're really interested in is pathways and mechanisms for de-risking investments. What's the right role of private capital, government funding, and government and industry partnerships, and how do we work together?

We have to solve this problem because the rate other countries are starting to innovate is much faster than what we’re seeing in the US, either because they don't have installed infrastructure or because they have better partnerships. We need to try to understand how to do this in the next five to 10 years, a short timescale.

Can you offer any specific examples of the cross-pollination that's happening with companies like Autodesk and GE Vernova at the table, along with Big Pharma companies?

Love: One of the benefits of a diverse group of participants around the table, both from the industry side and from our side at MIT across many different departments and schools, including our business school, the engineering school, and even our arts and humanities faculty, is we're all thinking about these questions.

Can we learn from one another? “See, here's a similar point or something that's a little different” or, "Oh! We solved this problem over here"? Those conversations have been rich and inviting, and we've done this through workshops and seminars. In the first week of May we’re hosting Manufacturing Week here at MIT. We'll have a number of opportunities to dig into that more with our partners as well as the broader community.

One example that I can point to is across a few sectors – electric vehicles, nuclear reactors, semiconductors, and biopharmaceuticals – is the idea of what I call gigafactories versus microfactories.

Microfactories offer an opportunity to think about small-volume, high-mix production. Gigafactories manufacture a large volume of products.

Take electric vehicles. It turns out that microfactories are a pretty interesting model now being explored. We're starting to see that microfactories, as you iterate and build new factories, you learn new ways to build the factory and to improve the process.

You're innovating as you go. If we look at Tesla, Rivian, and now emerging, Slate, each of these companies has learned how to make a more efficient factory. For biopharmaceuticals, we don't have that. We build a $2 billion facility, and we amortize it over 20 years, and then we think, "OK, what should we build now?"

Our factories today look very similar to the factories we built 20 years ago. There are examples of newer factories that have emerged, but our pace of innovation in manufacturing has been relatively slow. It might be interesting to think more about building, I might even say, "disposable factories," or otherwise smaller factories that we could learn from.

With the idea that biology is becoming more accessible, both through computation and tools like sequencing, the ability to think about biology as software is much more prominent today than it ever has been. A software-driven facility becomes a biology-driven facility or a software-driven facility that leverages biology — it's a natural interplay between what we're producing and the automation that can go around it.

As we look at rare diseases, AI-generated proteins, and all these ways in which we're starting to create more potent and targeted medicines, we are going to need solutions to bring those ideas to the market faster and in smaller volumes than what we have largely optimized around monoclonal antibody production.

About The Expert:

J. Christopher Love is the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering at MIT. He is a member of the Koch Institute for Integrative Cancer Research, an associate member at both the Broad Institute and the Ragon Institute of MGH, MIT, and Harvard. He earned a B.S. in chemistry from the University of Virginia and a Ph.D. in physical chemistry at Harvard University. He is a cofounder of OneCyte Biotechnologies, HoneyComb Biotechnologies, and Sunflower Therapeutics. He serves as an advisor to Repligen, QuantumCyte, and Alloy Therapeutics.